Zeude: Self-Hosted Monitoring Dashboard for Claude Code and OpenAI Codex
Zeude is a self-hosted monitoring dashboard that tracks Claude Code and OpenAI Codex usage in one place. The tool was developed after a team realized they couldn't track their Claude Code spending and couldn't find a suitable existing solution.
Key Features
The dashboard provides several practical monitoring capabilities:
- Per-prompt token and cost breakdowns for both Claude Code and OpenAI Codex
- Weekly leaderboard with cohort grouping for larger organizations
- Ability to push skills, MCP servers, and hooks to your entire team from the dashboard
- All data stays on your infrastructure
Version 1.0.0 Updates
The latest release adds three significant features:
- Windows support: Previously macOS/Linux only, now the whole team can use it regardless of OS
- Codex integration: Tracks both Claude Code and OpenAI Codex usage together, since many teams use both
- Per-user skill opt-out: Individuals can now turn off skills they don't want, addressing the limitation of all-or-nothing team skill sync
Technical Stack
The application is built with:
- Next.js
- Supabase
- ClickHouse
- OTel Collector
The tool was internally tested for approximately 6 months before being open-sourced. The developers note it's not perfect but solved their monitoring problem and might help others in similar situations.
📖 Read the full source: r/ClaudeAI
👀 See Also
Six open-source tools that address OpenClaw's security, cost, and complexity issues
A developer tested six community tools to solve OpenClaw's security gaps flagged by Cisco, spiraling costs, and complex setup. ClawSec provides security scanning and integrity verification, Antfarm enables deterministic multi-agent workflows, and LanceDB Pro improves memory retrieval with hybrid vector search.
OpenClaw Model Performance Review: Codex 5.3 Leads, GLM Models Disappoint
A developer tested multiple AI models with OpenClaw, finding Codex 5.3 performs best with 9/10 rating, while GLM 4.7 and GLM 5 scored 5/10 due to high token usage, slow responses, and inconsistent output.
AgentLens: Observability Tool for Multi-Agent AI Workflows
AgentLens provides unified tracing across Ollama, vLLM, Anthropic, and OpenAI, with cost tracking, an MCP server for querying stats from Claude Code, and a CLI for inline checks. It's self-hosted and runs locally via Docker.
Flash-MOE Benchmark on M5 Max: 12.99 tok/s with Qwen3.5-397B
A benchmark of the 397-billion-parameter Qwen3.5 model running locally on a MacBook Pro M5 Max with 128GB RAM achieved 12.99 tokens per second using 4-bit quantization and cache-io-split 4, three times faster than the original 48GB benchmark.